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Prefrontal cortex: categories, concepts
and cognitive control
Earl K. Miller
Picower Center for Learning and Memory,
RIKEN-MIT Neuroscience Research Center, and
Department of Brain and Cognitive Sciences,
Massachusetts Institute of Technology
www.millerlab.org
Executive (cognitive) control – The ability of the brain to wrest control
of its processing from reflexive reactions to the environment in order to
direct it toward unseen goals. Volition, goal-direction
Sensory
Motor
Basic sensory and motor functions
Sensory
Motor
Consolidation
(long-term storage)
Memories, habits and skills
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Executive Functions
goal-related information
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Top-down
Executive Functions
goal-related information
Selection
Bottom-up
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Top-down
Executive Functions
goal-related information
Selection
Bottom-up
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Top-down
Executive Functions
goal-related information
Selection
Bottom-up
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Top-down
Executive Functions
goal-related information
Selection
Bottom-up
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Top-down
Executive Functions
goal-related information
Selection
Bottom-up
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Top-down
Executive Functions
goal-related information
Selection
Bottom-up
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Top-down
Executive Functions
goal-related information
Selection
Bottom-up
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Top-down
Executive Functions
goal-related information
Selection
Bottom-up
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Top-down
Executive Functions
goal-related information
Selection
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Our Methods:
Train monkeys on tasks designed to isolate
cognitive operations related to executive control.
Record from groups of single neurons while
monkeys perform those tasks.
Top-down
Executive Functions
goal-related information
Selection
Bottom-up
(flexibility)
Sensory
Motor
Consolidation
(long-term storage)
Learning and memory
(Hippocampus, basal
ganglia, etc.)
Perceptual Categories
David Freedman
Maximillian Riesenhuber
Tomaso Poggio
Earl Miller
www.millerlab.org
Perceptual Categorization: “Cats” Versus “Dogs”
80% Cat
Morphs
60% Cat
Morphs
60% Dog
Morphs
80% Dog
Morphs
Prototypes
Prototypes
100% Dog
100% Cat
Category
boundary
Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2001) Science, 291:312-316
Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2002) J. Neurophysiology, 88:914-928.
Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K, (2003) J. Neuroscience, 23:5235-5246 .
“Cats”
Category boundary
“Dogs”
Delayed match to category task
.
.
.
.
RELEASE
(Category Match)
(Match)
Fixation
500 ms.
Sample
600 ms.
Delay
.
1000 ms.
Test object is a “match” if it the
same category (cat or dog) as the
sample
Test
(Nonmatch)
HOLD
(Category Non-match)
A “Dog Neuron” in the Prefrontal Cortex
Fixation
Sample
Delay
Test
13
Firing Rate (Hz)
P > 0.1
10
100% Dog
80:20 Dog:Cat
60:40 Dog:Cat
Cats vs. Dogs
P < 0.01
7
4
1
-500
100% Cat
80:20 Cat:Dog
60:40 Cat:Dog
0
500
1000
1500
2000
Time from sample stimulus onset (ms)
P > 0.1
To test the contribution of experience, we moved the category
boundaries and retrained a monkey
80% Cat
Morphs
60% Cat
Morphs
60% Dog
Morphs
80% Dog
Morphs
Prototypes
Prototypes
100% Dog
100% Cat
Category
boundary
To test the contribution of experience, we moved the category
boundaries and retrained a monkey
Old, now-irrelevant, boundary
New, now-relevant, boundary
PFC neural activity shifted to reflect the new boundaries
and no longer reflected the old boundaries
Old, now-irrelevant, boundary
New, now-relevant, boundary
Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2001)
Science, 291:312-316
Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2002)
J. Neurophysiology, 88:914-928
Posterior
Parietal
Cortex (PPC)
IPS
Cs
SPL
As
Lateral
Prefrontal
Cortex
(LPFC)
IPL
Parietal
Pathway
“where”
Ps
Ls
Temporal
Pathway
“what”
Sts
PIT
CIT
AIT
???
Inferior Temporal
Cortex (IT)
Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K, (2003)
J. Neuroscience, 23:5235-5246 .
Category Effects in the Prefrontal versus Inferior Temporal Cortex
Activity to individual stimuli along the 9 morph
lines that crossed the category boundary
C2
C3
C1
“cats”
Cats
Dogs
C1
D1
C1
D2
C1
D3
C2
D1
C2
D2
D3
D1
D3
D2
Cats
D1
D2
D3
D1
D2
D3
D1
D2
D3
C3
D2
C3
D3
C3
C2
C3
D1
Dogs
C1
C1
C1
C2
C2
C2
C3
C3
category boundary
“dogs”
PFC
ITC
C1
C1
C1
C2
D1
D2
D3
D1
C1
C1
C1
C2
D1
D2
D3
D1
C2
C2
C3
C3
D2
D3
D1
D2
C2
C2
C3
C3
D2
D3
D1
D2
C3
D3
C3
D3
0
0.5
Normalized firing rate
1.0
Category Effects were Stronger in the PFC than ITC: Population
50
PFC
ITC
60
Number of Neurons
Number of Neurons
70
50
40
30
20
40
30
20
10
10
0
-0.4
-0.2
0
0.2
0.4
0.6
Category Index Value Category
0
-0.4
-0.2
0
0.2
0.4
index valuesCategory Index Value
Stronger category effects
Index of the difference in activity to stimuli from different,
relative to same, category
0.6
Quantity (numerosity)
Andreas Nieder
David Freedman
Earl Miller
www.millerlab.org
Behavioral protocol: delayed-match-to-number task
Fixation
500 ms
Test
1200 ms
Match
Sample
800 ms
Release
Delay
1000 ms
Numbers 1 – 5
were used
Nonmatch
Tim
e
Hold
Preventing the monkey from memorizing visual patterns:
1. Position and size of dots shuffled pseudo-randomly.
2. Each numerosity tested with 100 different images per
session.
3. All images newly generated after a session.
4. Sample and test images never identical.
A. Nieder, D.J. Freedman, and E.K. Miller (2002) Science, 297:1708-1711.
Trained
Monkeys instantly
generalized across
the control stimulus
sets.
Standard stimulus
Equal area
Equal circumference
Low density
High density
Variable features
‘Shape’
Linear
Standard stimulus
Sample
Delay
Equal area
Spike rate (Hz)
1
2
3
4
5
standard
equal area
Spike rate (Hz)
30
20
Average sample interval activity
Time (ms)
10
1
2
3
Numerosity
4
5
Standard stimulus
Sample
Delay
Variable features
Spike rate (Hz)
1
2
3
4
5
20
standard
Spike rate (Hz)
variable features
15
10
Time (ms)
Average delay interval activity
5
1
2
3
Numerosity
4
5
Low density
Sample
Delay
High density
1
2
3
4
5
Spike rate (Hz)
10
high density
low density
Spike rate (Hz)
8
6
4
2
Average sample interval activity
Time (ms)
0
1
2
3
Numerosity
4
5
Characteristics of Numerosity
1. Preservation of numerical order – numbers are not isolated
categories.
2. Numerical Distance Effect – discrimination between numbers
improve with increasing distance between them
(e.g., 3 and 4 are harder to discriminate than 3 and 7)
100
Normalized response (%)
Normalized response (%)
PFC neurons show tuning curves for number.
75
50
25
0
0
2
4
6
8 10 12
Preferred numerosity
100
75
50
25
0
0
2
4
6
8 10 12
Preferred numerosity
Characteristics of Numerosity
1. Preservation of numerical order – numbers are not isolated
categories.
2. Numerical Distance Effect – discrimination between numbers
improve with increasing distance between them.
3. Numerical Magnitude Effect – discrimination between
numbers of equal numerical distance is increasingly difficult
as their size increases (e.g., 1 and 2 are easier to tell apart
than 5 and 6).
Numerical Magnitude Effect
Average width of population
tuning curves
Bandwidth of tuning curves
Average population tuning curve
for each number
Normalized response (%)
100
75
50
25
0
1
2
3
4
Numerosity
5
3.0
2.5
2.0
1.5
1.0
05
1
2
3
4
Numerosity
Neural tuning becomes increasing imprecise with increasing
number. Therefore, smaller size numbers are easier to
discriminate.
5
Scaling of numerical representations
Linear-coding hypothesis
Non-linear compression hypothesis
Amplitude
•symmetric distributions on linear scale (centered on numbers)
•wider distributions in proportion to increasing quantities
•symmetric distributions on a logarithmically compressed scale
•standard deviations of distributions constant across quantities
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0
0.0
2
4
6
8
10
12
14
16
2
18
20
N
um
berof item
s(logscale)
N
um
berof item
s(linearscale)
Amplitude
asymmetric on log scale
asymmetric on linear scale
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
2
10
N
um
berofitem
s(logscale)
0.0
0
2
4
6
8
10 12 14 16 18 20
N
um
berofitem
s(linearscale)
Non-linear scaling of behavioral data
80
60
40
monkey T
monkey P
average
20
0
1
2
3
4
5
6
7
8
9
10
*
11
2
Number of items (linear scale)
Logarithmic scaling
100
Performance (% correct)
1.00
Goodness-of-fit (r )
Performance (% correct)
100
80
0.95
0.90
60
linear pow(1/2)pow(1/3)
Scale
40
monkey T
monkey P
average
20
0
1
5
Number of items (log scale)
10
log
Non-linear scaling of neural data
80
60
40
1.00
20
0.95
2
Goodness-of-fit (r )
Normalized activity (%)
100
0
1
2
3
4
5
Number of items (linear scale)
Logarithmic scaling
0.90
0.85
0.80
0.75
linear pow(1/2)pow(1/3)
Scale
100
Normalized activity (%)
*
80
60
40
20
0
1
5
Number of items (log scale)
log
Scaling of numerical representations
Linear-coding hypothesis
Non-linear compression hypothesis
Amplitude
•symmetric distributions on linear scale (centered on numbers)
•wider distributions in proportion to increasing quantities
•symmetric distributions on a logarithmically compressed scale
•standard deviations of distributions constant across quantities
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0
0.0
2
4
6
8
10
12
14
16
2
18
20
N
um
berof item
s(logscale)
N
um
berof item
s(linearscale)
Amplitude
asymmetric on log scale
asymmetric on linear scale
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
2
10
N
um
berofitem
s(logscale)
0.0
0
2
4
6
8
10 12 14 16 18 20
N
um
berofitem
s(linearscale)
Scaling of numerical representations
Linear-coding hypothesis
•symmetric distributions on linear scale (centered on numbers)
•wider distributions in proportion to increasing quantities
Non-linear compression hypothesis
•symmetric distributions on a logarithmically compressed scale
•standard deviations of distributions constant across quantities
1.0
0.8
0.6
0.4
0.2
0.0
2
20
N
um
berof item
s(logscale)
asymmetric on log scale
asymmetric on linear scale
1.0
0.8
0.6
0.4
0.2
0.0
0
2
4
6
8
10 12 14 16 18 20
N
um
berofitem
s(linearscale)
Number-encoding neurons
A. Nieder and E.K. Miller
(in preparation)
Posterior
Parietal
Cortex (PPC)
A. Nieder, D.J. Freedman, and E.K. Miller (2002)
Science, 297:1708-1711.
Cs
IPS
SPL
As
Lateral
Prefrontal
Cortex
(LPFC)
IPL
Parietal
Pathway
“where”
Ps
Ls
Temporal
Pathway
“what”
Sts
PIT
CIT
AIT
Inferior Temporal
Cortex (IT)
A. Nieder and E.K. Miller
(in preparation)
Abstract number-encoding neurons
Parietal Cortex
N = 404
12
16 %
0%
3%
4%
7%
30 %
Cs
5
Lateral Prefrontal
Cortex
N = 352
5ip
As
VIP
7A
Ps
Ls
4%
Sts
AIT
Inferior Temporal Cortex
N = 77
Inferior Temporal Cortex
Standard stimulus
Low density
Equal circumference
25
High density
35
equal circumference
low density
high density
Spike rate (Hz)
Spike rate (Hz)
standard
20
15
10
30
25
20
1
2
3
Numerosity
4
5
1
2
3
Numerosity
4
5
Behavior-guiding Rules
Jonathan Wallis
Wael Asaad
Kathleen Anderson
Gregor Rainer
Earl Miller
www.millerlab.org
What is a rule?
Rules are conditional associations that describe the
logic of a goal-directed task.
CONCRETE
Asaad, Rainer, & Miller (1998)
(also see Fuster, Watanabe,
Wise et al)
ABSTRACT
Asaad, Rainer, & Miller (2000)
task context
Wallis et al (2001)
Release
Match Rule
(same)
Hold
Sample
Test
Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:953-956
Release
Hold
Sample
Test
Hold
Nonmatch Rule
(different)
Release
Sample
Test
Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:953-956
Release
Match Rule
(same)
Hold
Sample
Test
Hold
Nonmatch Rule
(different)
Release
Sample
Test
The rules were made abstract by training monkeys until they could
perform the task with novel stimuli
Sample + Cue
+ juice
OR
+ low tone
Match
+ no juice
OR
+ high tone
Nonmatch
Match Neuron
Cue
Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:953-956
Rule Representation in Other Cortical Areas
PMC
PFC
ITC
Timecourse of Rule-Selectivity Across the PFC Population:
Sliding ROC Analysis
ROC Value
PFC
Note: ROC Values are sorted by each time bin independently
Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology
Rule Representation in Other Cortical Areas
PMC
PFC
ITC
Abstract Rule-Encoding in Three Cortical Areas
PFC
Wallis, J.D. and Miller, E.K.
(in press) J. Neurophysiology
Abstract Rule-Encoding in Three Cortical Areas
PFC
Wallis, J.D. and Miller, E.K.
(in press) J. Neurophysiology
ITC
Wallis and Miller, in preparation
Abstract Rule-Encoding in Three Cortical Areas
PMC
PFC
Wallis, J.D. and Miller, E.K.
(in press) J. Neurophysiology
ITC
Wallis and Miller, in preparation
Wallis, J.D. and Miller, E.K.
(in press) J. Neurophysiology
Abstract Rule-Encoding was Stronger and Appeared Earlier in the PMC than PFC
ROC Value
PFC
PMC
Wallis and Miller, in press,
J. Neurophysiol.
Number of neurons
Median = 410
Median = 310
PMC
PFC
Latency for rule-selectivity (msec)
Abstract Rule-Encoding in Three Cortical Areas
PMC
PFC
Wallis, J.D. and Miller, E.K.
(in press) J. Neurophysiology
ITC
Wallis and Miller, in preparation
Wallis, J.D. and Miller, E.K.
(in press) J. Neurophysiology
CONCLUSIONS:
1. Goal-related information, including the categories and concepts needed for executive
control, is represented in the PFC while irrelevant details are largely discarded.
2. Neural representations of categories and concepts are stronger and more explicit in
the PFC than in cortical areas that provide the PFC with visual input (“cats and dogs”,
numbers). Highly familiar rules may be more strongly encoded in the PMC than PFC.
3. This ability of the PFC and related areas to convey categories, concepts and rules
may reflect their role in acquiring and representing the formal demands of tasks, the
internal models of situations and courses of action that provide a foundation for
complex, intelligent behavior.
A Model of PFC function:
Miller, E.K. (2000) The prefrontal cortex and cognitive control. Nature Reviews
Neuroscience, 1:59-65
Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function.
Annual Review of Neuroscience, 24:167-202
For reprints etc: www.millerlab.org
The PF cortex and cognitive control
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The prefrontal cortex may be like a switch operator
in a system of railroad tracks:
Its integrative anatomy allows it to rapidly acquire a
“map” that specifies which pattern of “tracks” (neural
pathways) are needed to solve a given task.
PF cortex
The prefrontal cortex may be like a switch operator
in a system of railroad tracks:
Its integrative anatomy allows it to rapidly acquire a
“map” that specifies which pattern of “tracks” (neural
pathways) are needed to solve a given task.
PF cortex
The PF cortex actively maintains this
pattern during task performance,
allowing feedback signals to bias the
flow of activity in other brain areas
along task-appropriate pathways.
GOAL-DIRECTION
FLEXIBILITY
Miller Lab @ MIT (www.millerlab.org)
Categories:
Other Miller Lab members:
David Freedman
Max Riesenhuber (Poggio lab)
Tomaso Poggio
Tim Buschman
Mark Histed
Christopher Irving
Cindy Kiddoo
Kristin Maccully
Michelle Machon
Anitha Pasupathy
Jefferson Roy
Melissa Warden
Numbers:
Andreas Nieder
David Freedman
Rules:
Jonathan Wallis
Wael Asaad
Kathy Anderson
Gregor Rainer